import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
A						=			{'LayoutLMv2Config', 'LayoutLMv3Config'}





@is_pipeline_test
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
		lowerCAmelCase_     =   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

		if model_mapping is not None:
				lowerCAmelCase_     =   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
		if tf_model_mapping is not None:
				lowerCAmelCase_     =   {
				    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
				}
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Dict     ,  snake_case__     :     Any     ,  snake_case__     :     Tuple		)      ->	Tuple:
				_lowerCamelCase				   =  ZeroShotClassificationPipeline(
				    model=snake_case__     ,  tokenizer=snake_case__     ,  candidate_labels=['polics', 'health']		)
				return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     str		)      ->	Optional[int]:
				_lowerCamelCase				   =  classifier('Who are you voting for in 2020?'     ,  candidate_labels='politics'		)
				self.assertEqual(snake_case__     ,  {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		)], 'scores': [ANY(snake_case__		)]}		)

				# No kwarg
				_lowerCamelCase				   =  classifier('Who are you voting for in 2020?'     ,  ['politics']		)
				self.assertEqual(snake_case__     ,  {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		)], 'scores': [ANY(snake_case__		)]}		)

				_lowerCamelCase				   =  classifier('Who are you voting for in 2020?'     ,  candidate_labels=['politics']		)
				self.assertEqual(snake_case__     ,  {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		)], 'scores': [ANY(snake_case__		)]}		)

				_lowerCamelCase				   =  classifier('Who are you voting for in 2020?'     ,  candidate_labels='politics, public health'		)
				self.assertEqual(
				    snake_case__     ,  {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		), ANY(snake_case__		)], 'scores': [ANY(snake_case__		), ANY(snake_case__		)]}		)
				self.assertAlmostEqual(sum(nested_simplify(outputs['scores']		)		)     ,  1.0		)

				_lowerCamelCase				   =  classifier('Who are you voting for in 2020?'     ,  candidate_labels=['politics', 'public health']		)
				self.assertEqual(
				    snake_case__     ,  {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		), ANY(snake_case__		)], 'scores': [ANY(snake_case__		), ANY(snake_case__		)]}		)
				self.assertAlmostEqual(sum(nested_simplify(outputs['scores']		)		)     ,  1.0		)

				_lowerCamelCase				   =  classifier(
				    'Who are you voting for in 2020?'     ,  candidate_labels='politics'     ,  hypothesis_template='This text is about {}'		)
				self.assertEqual(snake_case__     ,  {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		)], 'scores': [ANY(snake_case__		)]}		)

				# https://github.com/huggingface/transformers/issues/13846
				_lowerCamelCase				   =  classifier(['I am happy']     ,  ['positive', 'negative']		)
				self.assertEqual(
				    snake_case__     ,  [
				        {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		), ANY(snake_case__		)], 'scores': [ANY(snake_case__		), ANY(snake_case__		)]}
				        for i in range(1		)
				    ]     ,  )
				_lowerCamelCase				   =  classifier(['I am happy', 'I am sad']     ,  ['positive', 'negative']		)
				self.assertEqual(
				    snake_case__     ,  [
				        {'sequence': ANY(snake_case__		), 'labels': [ANY(snake_case__		), ANY(snake_case__		)], 'scores': [ANY(snake_case__		), ANY(snake_case__		)]}
				        for i in range(2		)
				    ]     ,  )

				with self.assertRaises(snake_case__		):
						classifier(''     ,  candidate_labels='politics'		)

				with self.assertRaises(snake_case__		):
						classifier(snake_case__     ,  candidate_labels='politics'		)

				with self.assertRaises(snake_case__		):
						classifier('Who are you voting for in 2020?'     ,  candidate_labels=''		)

				with self.assertRaises(snake_case__		):
						classifier('Who are you voting for in 2020?'     ,  candidate_labels=snake_case__		)

				with self.assertRaises(snake_case__		):
						classifier(
						    'Who are you voting for in 2020?'     ,  candidate_labels='politics'     ,  hypothesis_template='Not formatting template'     ,  )

				with self.assertRaises(snake_case__		):
						classifier(
						    'Who are you voting for in 2020?'     ,  candidate_labels='politics'     ,  hypothesis_template=snake_case__     ,  )

				self.run_entailment_id(snake_case__		)
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     Pipeline		)      ->	int:
				_lowerCamelCase				   =  zero_shot_classifier.model.config
				_lowerCamelCase				   =  config.labelaid
				_lowerCamelCase				   =  zero_shot_classifier.entailment_id

				_lowerCamelCase				   =  {'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
				self.assertEqual(zero_shot_classifier.entailment_id     ,  -1		)

				_lowerCamelCase				   =  {'entailment': 0, 'neutral': 1, 'contradiction': 2}
				self.assertEqual(zero_shot_classifier.entailment_id     ,  0		)

				_lowerCamelCase				   =  {'ENTAIL': 0, 'NON-ENTAIL': 1}
				self.assertEqual(zero_shot_classifier.entailment_id     ,  0		)

				_lowerCamelCase				   =  {'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
				self.assertEqual(zero_shot_classifier.entailment_id     ,  2		)

				_lowerCamelCase				   =  original_labelaid
				self.assertEqual(snake_case__     ,  zero_shot_classifier.entailment_id		)
		@require_torch
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Dict:
				_lowerCamelCase				   =  pipeline(
				    'zero-shot-classification'     ,  model='sshleifer/tiny-distilbert-base-cased-distilled-squad'     ,  framework='pt'     ,  )
				# There was a regression in 4.10 for this
				# Adding a test so we don't make the mistake again.
				# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
				zero_shot_classifier(
				    'Who are you voting for in 2020?' * 1_0_0     ,  candidate_labels=['politics', 'public health', 'science']		)
		@require_torch
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	int:
				_lowerCamelCase				   =  pipeline(
				    'zero-shot-classification'     ,  model='sshleifer/tiny-distilbert-base-cased-distilled-squad'     ,  framework='pt'     ,  )
				_lowerCamelCase				   =  zero_shot_classifier(
				    'Who are you voting for in 2020?'     ,  candidate_labels=['politics', 'public health', 'science']		)

				self.assertEqual(
				    nested_simplify(snake_case__		)     ,  {
				        'sequence': 'Who are you voting for in 2020?',
				        'labels': ['science', 'public health', 'politics'],
				        'scores': [0.333, 0.333, 0.333],
				    }     ,  )
		@require_tf
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Any:
				_lowerCamelCase				   =  pipeline(
				    'zero-shot-classification'     ,  model='sshleifer/tiny-distilbert-base-cased-distilled-squad'     ,  framework='tf'     ,  )
				_lowerCamelCase				   =  zero_shot_classifier(
				    'Who are you voting for in 2020?'     ,  candidate_labels=['politics', 'public health', 'science']		)

				self.assertEqual(
				    nested_simplify(snake_case__		)     ,  {
				        'sequence': 'Who are you voting for in 2020?',
				        'labels': ['science', 'public health', 'politics'],
				        'scores': [0.333, 0.333, 0.333],
				    }     ,  )
		@slow
		@require_torch
		def   _snake_case       (       self     :     Any		)      ->	str:
				_lowerCamelCase				   =  pipeline('zero-shot-classification'     ,  model='roberta-large-mnli'     ,  framework='pt'		)
				_lowerCamelCase				   =  zero_shot_classifier(
				    'Who are you voting for in 2020?'     ,  candidate_labels=['politics', 'public health', 'science']		)

				self.assertEqual(
				    nested_simplify(snake_case__		)     ,  {
				        'sequence': 'Who are you voting for in 2020?',
				        'labels': ['politics', 'public health', 'science'],
				        'scores': [0.976, 0.015, 0.009],
				    }     ,  )
				_lowerCamelCase				   =  zero_shot_classifier(
				    'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
				    ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
				    ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
				    ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
				    ' machine translation tasks show these models to be superior in quality while being more parallelizable'
				    ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
				    ' English-to-German translation task, improving over the existing best results, including ensembles by'
				    ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
				    ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
				    ' fraction of the training costs of the best models from the literature. We show that the Transformer'
				    ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
				    ' large and limited training data.'     ,  candidate_labels=['machine learning', 'statistics', 'translation', 'vision']     ,  multi_label=snake_case__     ,  )
				self.assertEqual(
				    nested_simplify(snake_case__		)     ,  {
				        'sequence': (
				            'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
				            ' networks in an encoder-decoder configuration. The best performing models also connect the'
				            ' encoder and decoder through an attention mechanism. We propose a new simple network'
				            ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
				            ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
				            ' superior in quality while being more parallelizable and requiring significantly less time to'
				            ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
				            ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
				            ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
				            ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
				            ' costs of the best models from the literature. We show that the Transformer generalizes well to'
				            ' other tasks by applying it successfully to English constituency parsing both with large and'
				            ' limited training data.'
				        ),
				        'labels': ['translation', 'machine learning', 'vision', 'statistics'],
				        'scores': [0.817, 0.713, 0.018, 0.018],
				    }     ,  )



		@slow
		@require_tf
		def   _snake_case       (       self     :     Optional[int]		)      ->	Dict:
				_lowerCamelCase				   =  pipeline('zero-shot-classification'     ,  model='roberta-large-mnli'     ,  framework='tf'		)
				_lowerCamelCase				   =  zero_shot_classifier(
				    'Who are you voting for in 2020?'     ,  candidate_labels=['politics', 'public health', 'science']		)

				self.assertEqual(
				    nested_simplify(snake_case__		)     ,  {
				        'sequence': 'Who are you voting for in 2020?',
				        'labels': ['politics', 'public health', 'science'],
				        'scores': [0.976, 0.015, 0.009],
				    }     ,  )
				_lowerCamelCase				   =  zero_shot_classifier(
				    'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
				    ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
				    ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
				    ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
				    ' machine translation tasks show these models to be superior in quality while being more parallelizable'
				    ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
				    ' English-to-German translation task, improving over the existing best results, including ensembles by'
				    ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
				    ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
				    ' fraction of the training costs of the best models from the literature. We show that the Transformer'
				    ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
				    ' large and limited training data.'     ,  candidate_labels=['machine learning', 'statistics', 'translation', 'vision']     ,  multi_label=snake_case__     ,  )
				self.assertEqual(
				    nested_simplify(snake_case__		)     ,  {
				        'sequence': (
				            'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
				            ' networks in an encoder-decoder configuration. The best performing models also connect the'
				            ' encoder and decoder through an attention mechanism. We propose a new simple network'
				            ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
				            ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
				            ' superior in quality while being more parallelizable and requiring significantly less time to'
				            ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
				            ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
				            ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
				            ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
				            ' costs of the best models from the literature. We show that the Transformer generalizes well to'
				            ' other tasks by applying it successfully to English constituency parsing both with large and'
				            ' limited training data.'
				        ),
				        'labels': ['translation', 'machine learning', 'vision', 'statistics'],
				        'scores': [0.817, 0.713, 0.018, 0.018],
				    }     ,  )
from abc import ABC, abstractmethod
from argparse import ArgumentParser





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		@staticmethod
		@abstractmethod
		def   _snake_case       (       snake_case__     :     ArgumentParser		)      ->	Tuple:
				raise NotImplementedError()



		@abstractmethod
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Optional[Any]:
				raise NotImplementedError()